Pexs Mb Mb Pexs Mb Mb ( Pex3, Pex11, and Pex14) were visualized in numerous vesicles distinct from hydrogenosomes, the endoplasmic reticulum (ER), and Golgi complex. Proteomic analysis of cellular fractions and prediction of peroxisomal targeting signals (PTS1/PTS2) identified 51 putative peroxisomal matrix proteins. Expression of Saccharomyces cerevisiae selected proteins in revealed specific targeting to peroxisomes. The matrix proteins identified included components of acyl-CoA and carbohydrate metabolism and pyrimidine and CoA biosynthesis, wh ereas no components related to either β -oxidation or catalase were present. In conclusion, we identified a subclass of peroxisomes, named 'anaerobic' peroxisomes that shift the current paradigm and turn attention to the reductive evolution of peroxisomes in anaerobic organisms.

Lee, J.E., Edwards, J.S., Schmitt, J., Fischer, H., Bock, M., Brook, E.J., 2020. Excess methane in Greenland ice cores associated with high dust concentrations. Geochimica et Cosmochimica Acta 270, 409-430.

http://www.sciencedirect.com/science/article/pii/S0016703719307288 Ice core records of atmospheric methane (CH ) and its isotopic composition provide important information 4 about biogeochemical cycles in the past. Interpreting these data requires that they faithfully record the composition of the atmosphere. In this study, we describe anomalies of up to 30 -40 ppb CH that are only 4 ∼ observed in dust-rich (> 60 ng Ca/g ice), glacial -period ice measured with standard melt-refreeze methods. The stable isotopic composition of CH is also significantly affected. Results from the GISP2 and NEEM ice cores 4 from Greenland show that excess CH is either released or produced in the presence of liquid water in amounts 4 2+ which are highly correlated with the abundance of Ca and mineral dust in the sample. Additional experiments show that excess CH is unaffected by the addition of HgCl (a microbial inhibitor) and is not related to ice core 4 2 storage time. Dust concentrations in Antarctic ice cores are an order of magnitude lower than in Greenlandic ice cores and no excess CH was observed in samples from the Antarctic WAIS Divide (WD) and South Pole 4 (SPICE) ice cores. While the overall structure of the ice core atmospheric methane history is minimally impacted by excess CH , the impacts on the isotopic record and on inverse models used to reconstruct CH 4 4 sources are greater. We propose three potential mechanisms to explain the presence of excess CH : (1) that CH 4 4 is adsorbed on dust particles prior to deposition on the ice sheet and is slowly desorbed during the melt- extraction step of sample analysis; (2) that dust acts as a micro-environment within the ice sheet for methanogenic extremophiles; or (3) that excess CH is a product of abiotic degradation of organic compounds 4 during the melt-extraction step of sample analysis.

Lee, K.J., 2020. Characterization of kerogen content and activation energy of decomposition using machine learning technologies in combination with numerical simulations of formation heating. Journal of Petroleum Science and Engineering 188, 106860.

http://www.sciencedirect.com/science/article/pii/S0920410519312768 Reliable estimation of organic matter characteristics is essential in source and reservoir rocks evaluation. Their measurement is generally based on well logging and experiments after core sampling. In this study, we present a different approach from these methods to evaluate the characteristics of organic matter, which is based on the numerical simulations in combination with machine learning technologies. Formation heating processes of organic-rich shales and in-situ pyrolysis of kerogen are numerically simulated, where the time-series data of heater temperature are monitored. Here, we consider the whole organic matter composed with kerogen. The monitored heater temperature data are used as an input of inverse modeling by Artificial Neural Network (ANN) and Support Vector Machines (SVM) to figure out the characteristics of kerogen. Heater temperature acts as an indicator of type and maturity of kerogen, since it is affected by the bulk thermal conductivity of formation, which is a function of dynamically changing rock-and-pore composition by kerogen decomposition. We simulate the processes of electrical heating of 300 different formations, containing either one of Type 1, 2, or 3 kerogen with various amounts and activation energies. ANN method is employed to generate a data-driven model to estimate the unknown kerogen content and activation energy using heater temperature as an input data. SVM method, which categorizes dataset into the multiple classes by using hyperplanes, is applied to classify the